Discover how BERT works and implement it in Python to build advanced NLP solutions, including automatic question and answer systems.
BERT revolutionized natural language processing by changing how artificial intelligence models understand context and text meaning. In this course you'll learn how this model works, from its theoretical foundations to its practical implementation with Python, TensorFlow and Google Colab, with no need for complex configurations.
The course is designed so you can follow it even if you've never worked with NLP models. We'll start by reviewing the essential concepts and the evolution of natural language processing techniques up to BERT, explaining each step clearly so you understand not just how to use the model, but why it works.
Throughout the lessons you'll implement BERT in Python using the most modern tools from the deep learning ecosystem. You'll work with TensorFlow and Google Colab to focus on learning and experimenting, avoiding installation issues, compatibility problems, or development environment configuration headaches.
Beyond understanding the theory, you'll apply BERT to real-world natural language processing problems, such as text classification, semantic analysis, or automatic question-and-answer systems. By the end of the course, you'll have a solid foundation to incorporate advanced language models into your own artificial intelligence projects.
Discover the evolution of natural language processing and understand why BERT revolutionized NLP and marked a turning point in Artificial Intelligence.
Learn how BERT processes language using bidirectional attention and what sets it apart from traditional models like RNNs and CNNs.
Use BERT's tokenization tools to clean, transform, and prepare text datasets ready for training AI models.
Learn to reuse pretrained models and adapt them to specific tasks to get excellent performance with less data and training time.
Implement BERT-based solutions for text classification, semantic analysis, question-answering systems, and other common natural language processing tasks.
Implement all examples using TensorFlow 2 and Google Colab, avoiding installation issues and taking advantage of a modern, Deep Learning-ready environment.
Learn how to download, explore, and integrate pre-trained models and official resources to speed up your NLP project development.
Build your own layers and architectures that incorporate BERT as a foundation to solve specific natural language processing problems.
Combine theory with practice to understand, implement, and adapt one of the most influential language models in the history of Artificial Intelligence.
Ligency Team is an international team of experts in programming, artificial intelligence, data science and technology, founded by Kirill Eremenko and Hadelin de Pontevés, creators of some of the world's most popular courses in these disciplines. With millions of students and a multidisciplinary team of over 20 professionals, our mission is to offer practical, high-quality training that helps people at any level develop relevant technological skills. Since 2018, our courses have been available in Spanish thanks to the collaboration with Juan Gabriel Gomila and Frogames Formación, bringing the best educational content to the Spanish-speaking community.
Mathematician, Certified Unity Instructor, and Online Instructor who has trained over 600,000 students worldwide across different platforms such as Udemy and Platzi. CEO of Frogames Formación and driving force behind this platform, bringing all his knowledge in Mathematics, Machine Learning, Videogames, AI and Blockchain among others.
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